Telecommunication Networks and integrated Services (TNS)
Laboratory
Department of Digital SystemsUniversity of Piraeus Research Center (UPRC)
University of Piraeus
Green Footprint
Prof. P, Demestichas, Assist. Prof. A Rouskas,
M. Logothetis
Email: {pdemest, arouskas, mlogothe} @unipi.gr
http://tns.ds.unipi.gr/
TNS – Green Footprint
Outline
Introduction - Research Areas - Motivation
Energy efficient Resource Allocation to femtocells Problem Statement
Proposed Solution
Indicative Results
Conclusion – Future Work
Operator-driven Traffic Engineering in Core Networks Problem Formulation
Proposed Solution
Indicative Results
Conclusion – Future Work
Disseminations
2
TNS – Green Footprint
Introduction / Research Areas
3
Research Areas Wireless Access
High-speed, wireless-access, infrastructures (2G, 3G, B3G, 4G).
Fixed Access – Core Network Optical Networks (WDM, SONET)
Fixed access networks (xDSL, FTTx,)
Emerging wireless world
TNS – Green Footprint
Motivation
The estimation for 2020 : mobile communication infrastructures will
represent more than 50% of network CO2 emissions.
Need for reduction of transmission powers and energy consumption in
Wireless and Fixed Access
4
Global telecoms footprint [2002 & 2020]
TNS – Green Footprint
Problem Statement
5
Problematic situation All terminals are served through the BS Congestion issues arise Inadequate QoS (delivery probability, delay, etc.)
to the terminals
Femtocells are the opportunity that is exploited They offer their resources for the relief of the
congested BS
Opportunistic Network Creation Terminals are offloaded to femtocells BS is no longer congested Terminals experience higher QoS
Energy efficiency Femtocells are configured to operate at the minimum
possible power level required to cover the terminals Switch off femtocells that have not acquired traffic
Opportunistic Networks are operator governed
extensions of the infrastructure
TNS – Green Footprint 6
Process:
1. Selection of femtocells which are nearest to the terminals that will participate in the ON
2. Initial configuration of femtocells to the max power level
3. Assignment of traffic to femtocells
4. Selection of femtocells that can decrease their power level
5. Gradually decrease the power level of each femtocell to the minimum level that the constraints (coverage and capacity) are not violated
Solution - Energy efficient Resource Allocation to femtocells
Input:The congested BS and its capabilities: RAT, Capacity, CoverageSet of deployed femtocells and their capabilities : RAT, Capacity, Set of possible transmission powersTerminals information: RAT, Location, Mobility level, Sensitivity
Output:The allocation of transmission powers to the femtocells The assignment of terminals to femtocells
TNS – Green Footprint
Indicative Results [1/4]
7
The delivery probability Increases after the solution
enforcement
Increases as more terminals are offloaded to the femtocells
Decreases as the terminals’ mobility level increases
The delay Decreases after the solution
enforcement
Decreases as more terminals are offloaded to the femtocells
Increases as the terminals’ mobility level increases
TNS – Green Footprint
Indicative Results [2/4]
8
Power and traffic allocation to the femtocells
- For central user distribution many femtocells remain without traffic and are switched off
- For sparse user distribution more terminals need to remain active to cover the traffic
Output of Algorithm
TNS – Green Footprint
Indicative Results [3/4]
9
BS energy consumption in relation with the number of femtocells Energy consumption decreases
as more femtocells are deployed
BS energy consumption in relation with the number of serving terminals Energy consumption rises while
more terminals are served through the BS
TNS – Green Footprint
Indicative Results [1/4]
10
Femto-terminals need low transmission power to communicate with the femtocells Increased battery lifetime
(25% in average)
Battery’s residual capacity drops at lower rate
TNS – Green Footprint
Conclusions – Future Work
11
Conclusions The algorithm
Allocates the minimum possible transmission power to femtocells that is needed to cover the terminals that are suitable to be offloaded to femtocells
Switches off the femtocells that remain without traffic
Femtocells are an energy efficient solution
Decreased BS power consumption due to the redirection of a proportion of the terminals
Increased battery lifetime of femto-terminals due to the small distance between terminals and femtocells
Future Work
Frequency allocation by taking into account interferences from neighboring BSs in a general sense
Taking into account QoS requirements
TNS – Green Footprint
Operator-driven Traffic Engineering in Core Networks
12
Computation of optimum
routing configuratio
n
Monitoring
Setting LSPs
Policy
RAN request
s
Video Servers
Ingress LSRs
Egress LSRs
Base Stations
Operator Problem Statement: find the
most suitable routing configuration to accommodate traffic demands, satisfying operator’s policies
Proposed Solution:
CORE - Multilayer Traffic Engineering: IP/MPLS over DWDM (for optical core networks)
TNS – Green Footprint
Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks
13
Problem Statement: find the most energy-efficient lightpath to accommodate the new traffic demand, while respecting the capacity of fibers and wavelengths.
Proposed Solution (CORE - Multilayer Traffic Engineering: IP/MPLS over DWDM)
Energy efficiency is achieved through the allocation of traffic to dedicated lightpaths, which are restricted at the optical layer only (optical bypass), when this is possible. Our proposed heuristic algorithm (ETAL) activates and exploits more network elements in order to find the necessary portions for establishing lightpaths without aggregating them.
TNS – Green Footprint
Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks
14
Find all paths
Order Paths
Find optimal lightpath Minimum conversions
Dedicated lightpath
Optical bypassing
Enforce decision GMPLS signaling
Update network’s status
Heuristic Algorithm: Energy-aware allocation of traffic to lightpaths (ETAL)
TNS – Green Footprint
Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks
15
Evaluation: comparisons with energy-efficient routing schemes
Metrics: number of conversions, consumed power, number of activated fibers, number of activated wavelengths, number of activated paths, average length of activated paths
Future WorkDevelop an updated cost function which will include proactive approach
TNS – Green Footprint
Disseminations
• D. Karvounas, A. Georgakopoulos, D. Panagiotou, V. Stavroulaki, K. Tsagkaris, P. Demestichas, “Achieving energy efficiency through the opportunistic exploitation of resources of infrastructures comprising cells of various sizes”, Journal of Green Engineering, vol.2, issue 3, River Publishers, 2012
D. Karvounas, A. Georgakopoulos, V. Stavroulaki, N. Koutsouris, K. Tsagkaris, P. Demestichas, “Resource Allocation to Femtocells for Coordinated Capacity Expansion of Wireless Access Infrastructures”, accepted for publication at EURASIP Journal on Wireless Communications and Networking, Special Issue on Femtocells in 4G Systems, 2012
V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas, “Energy Savings with Multilayer Traffic Engineering in Future Core Networks”, Journal of Green Engineering, 2012.
V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas, “Energy-Aware Allocation of Traffic to Optical Lightpaths in Multilayer Core Networks”, submitted for publication to IEEE/OSA Journal of Lightwave Technology, 2012.
V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas,” Energy Savings in Multilayer Core Networks”,submitted for publication to IEEE International Conference on Communications, 2012.
16
Top Related